What is Continuous Data? Everything You Need To Know

Are you a runner by chance? If so, do you track your distances and times regularly? What are your weekly kilometer/mile totals?

I’m sure that if you measure them each week you’ll get different numbers like 30km, 40km, or even 50km. These results are continuous data. Basically, it’s all about numbers that can be any value within a range.

That being said, let’s move on to a deeper explanation of what continuous data is.

What is Continuous Data

Continuous data is information that can vary within a range and is measured precisely. Unlike categorical data with its separate values, continuous data is unbroken and can have endless possibilities. Examples of continuous data include measurements like height, or time – they change smoothly without breaks or gaps, showcasing continuous data.

Examples of continuous data:

Measurements: Distance traveled, weight of an object or gas mileage of a car
Time intervals: Time taken to complete a task, sales revenue over time, or duration of a movie

Examples of Continuous Data on the xAxis

Continuous data can be presented in at least a dozen different ways.

Instead of qualitative labels, the x-axis features continuous data with numerical labels that denote specific points on a given scale. These labels provide specific reference points for interpreting the data.

PRO TIP: If your data can vary within a range then it’s definitely continuous data.

Now, a few questions arise. How can you use the data to the fullest?

How To Get The Best Out Of Your Continuous Data

To get the best out of your continuous data, you need to focus on two things:

Appropriate chart type
Readability, as it’s very easy to mess it up with continuous data

This sounds very similar to categorical data. Let’s start with the best chart type.

Chart Types for Continuous Data

The best chart types for continuous data are those that can easily display a continuous range of values. Here are 3 groups of charts you should consider for continuous data:

Line Chart

A line chart is a visualization that represents data in points called ‘marker’s connected by straight line segments. It’s perhaps the most common choice for continuous data as a line chart can take on any value within a certain range.

Who needs weather forecasts when you’ve got this chart?

Line chart is:

Best for identifying trends and changes over time
Great for small amounts of data series
A very popular chart – making it easy to understand

However, you should be careful with larger amounts of series as they can make the chart overcrowded.

Area Chart

An area chart is basically a line chart with one subtle difference. The area between the line and the horizontal axis is filled in with color. This type of chart is more commonly used for cumulative totals.

Can you guess what those two regions are?

An area chart is:

Useful for showing overlapping trends like temperature variations by region. Each region is represented by a different colored area, overlapping areas indicate periods where temperatures in multiple regions are similar
Ideal for cumulative values like population growth. Each area would represent the total population up to a specific year
Great for highlighting the magnitude of values

Avoid using area charts when you don’t have cumulative data since line charts should be your first choice non cumulative data.

Heatmap Chart

A heatmap chart is like a color-coded table where each cell represents a point. The colors in the cells visualize the values of the data. A heatmap chart is often used to show patterns in large sets of data.

This one is a bit special as it might work quite well for categorical data too. Still it’s also perfect for a variety of continuous data like temperature variations across geographic regions.

Hot or cold? This heatmap makes it easy to spot the temperature trends!

Heatmap chart is:

Great for pattern detection in large datasets using color-coded cells
Ideal for condensing complex data into a visually intuitive format
A fresh and modern type of chart that matches well with the latest UI designs

Remember that you can also use heatmap charts for categorical data like a leaderboard for the best football strikers – it would work brilliantly there too.

The data might be a little “creative,” but in this fantasy world, Robert Lewandowski is still the GOAT!

2 Powerful Tips for Visualizing Continuous Data

Knowing the best chart types for continuous data, we only have one topic left to cover today. How can you keep chart readability as high as possible with this simple but demanding type of data?

1. Clear and contrasting series
Sometimes, even if the colors are quite similar, we can distinguish the series easily. How? By adding dashed or dotted lines to the chart. You can read more about it in our article about colors here.

If you need to use similar colors in your chart, you can differentiate the series by changing the line styles.

2. Use annotations to provide additional context (works exceptionally well for area charts)
Enhance the readability of your visualizations by incorporating annotations along the axis. Annotations provide additional context, guiding the viewer’s attention to critical points in the data.

Annotations are an excellent way to highlight the most important results from a chart.

Do you want to find out more?
Just take a look at our article about categorical data. There are plenty of best practices that you can apply in your own data visualizations.

Continuous Data Overview

Continuous data is information that can vary within a range and is measured precisely.
Examples of continuous data include measurements like height, or time.
Best charts for continuous data are:
1. Line charts
2. Area charts
3. Heatmap charts
Always pay attention to the readability of your data. Keep the colors clear and contrasting. Don’t hesitate to use annotations to explain complex data relationships.

The Bottom Line

If your data can vary within a range then it’s definitely continuous data. It has plenty of usages including measurements like height, or time.

There are 3 groups of charts that are particularly effective for continuous data. Line charts are best for identifying trends and changes over time, while area charts are better for cumulative values like population growth. For other use cases, heatmap charts are great for pattern detection in large datasets using color-coded cells.

Don’t forget about keeping those charts readability at its highest by ensuring that colors are contrasting and don’t hesitate to use annotations to explain complex data relationships.